Code
library(readr)
library(ggplot2)
library(sf)
library(rnaturalearth)
library(rnaturalearthdata)
library(dplyr)
library(gridExtra)
library(biscale)
library(colorspace)
library(grid)
library(jsonlite)
library(here)library(readr)
library(ggplot2)
library(sf)
library(rnaturalearth)
library(rnaturalearthdata)
library(dplyr)
library(gridExtra)
library(biscale)
library(colorspace)
library(grid)
library(jsonlite)
library(here)# Read both RDS files from the Data folder
continental_data <- readRDS(here::here("Data/FUSE_continental_full_results_averaged_budget0.3_replicates10.rds"))
high_seas_data <- readRDS(here::here("Data/FUSE_full_highseas_results_averaged_budget0.3_replicates10.rds"))
# Get world map data
world <- ne_countries(scale = "medium", returnclass = "sf")
# Define the McBryde-Thomas 2 projection
mcbryde_thomas_2 <- "+proj=mbt_s"
# Transform both datasets to sf objects and project
continental_sf <- st_as_sf(continental_data, coords = c("Longitude", "Latitude"), crs = 4326) %>%
st_transform(crs = mcbryde_thomas_2)
high_seas_sf <- st_as_sf(high_seas_data, coords = c("Longitude", "Latitude"), crs = 4326) %>%
st_transform(crs = mcbryde_thomas_2)
# Combine the datasets
combined_sf <- rbind(
mutate(continental_sf, Region = "Continental Waters"),
mutate(high_seas_sf, Region = "High Seas")
)
# Project the world map
world_projected <- st_transform(world, crs = mcbryde_thomas_2)
# Create the globe bounding box
globe_bbox <- rbind(c(-180, -90), c(-180, 90),
c(180, 90), c(180, -90), c(-180, -90))
# Create the globe border
globe_border <- st_polygon(list(globe_bbox)) %>%
st_sfc(crs = 4326) %>%
st_sf(data.frame(rgn = 'globe', geom = .)) %>%
smoothr::densify(max_distance = 0.5) %>%
st_transform(crs = mcbryde_thomas_2)
# Create base theme
my_theme <- theme_minimal() +
theme(
legend.position = "bottom",
legend.direction = "horizontal",
legend.box = "vertical",
legend.margin = margin(t = 20, r = 0, b = 0, l = 0),
legend.title = element_text(margin = margin(b = 10)),
panel.background = element_rect(fill = "white", color = NA),
plot.background = element_rect(fill = "white", color = NA),
panel.grid = element_blank()
)
# 1. Continental Waters Plot
continental_plot <- ggplot() +
geom_sf(data = continental_sf, aes(color = Priority), size = 0.5, alpha = 0.7) +
geom_sf(data = world_projected, fill = "lightgrey", color = "lightgrey", size = 0.1) +
geom_sf(data = globe_border, fill = NA, color = "black", size = 0.5) +
scale_color_gradientn(
colors = c("white", "yellow", "darkblue"),
values = c(0, 0.5, 1),
name = "Priority",
guide = guide_colorbar(barwidth = 20, barheight = 0.5,
title.position = "top", title.hjust = 0.5)
) +
labs(title = "Conservation Priorities in Continental Waters",
subtitle = "Index: FUSE, Budget: 0.3, Replicates: 10",
x = NULL, y = NULL) +
my_theme
# 2. High Seas Plot
high_seas_plot <- ggplot() +
geom_sf(data = high_seas_sf, aes(color = Priority), size = 0.5, alpha = 0.7) +
geom_sf(data = world_projected, fill = "lightgrey", color = "lightgrey", size = 0.1) +
geom_sf(data = globe_border, fill = NA, color = "black", size = 0.5) +
scale_color_gradientn(
colors = c("white", "yellow", "darkblue"),
values = c(0, 0.5, 1),
name = "Priority",
guide = guide_colorbar(barwidth = 20, barheight = 0.5,
title.position = "top", title.hjust = 0.5)
) +
labs(title = "Conservation Priorities in High Seas",
subtitle = "Index: FUSE, Budget: 0.3, Replicates: 10",
x = NULL, y = NULL) +
my_theme
# Combined Plot (modified)
combined_plot <- ggplot() +
geom_sf(data = combined_sf,
aes(color = Priority),
size = 0.5,
alpha = 0.7) +
geom_sf(data = world_projected, fill = "lightgrey", color = "lightgrey", size = 0.1) +
geom_sf(data = globe_border, fill = NA, color = "black", size = 0.5) +
scale_color_gradientn(
colors = c("white", "yellow", "darkblue"),
values = c(0, 0.5, 1),
name = "Priority",
guide = guide_colorbar(barwidth = 20, barheight = 0.5,
title.position = "top", title.hjust = 0.5)
) +
labs(title = "Combined Conservation Priorities",
subtitle = "Continental Waters and High Seas\nIndex: FUSE, Budget: 0.3, Replicates: 10",
x = NULL, y = NULL) +
my_theme
# Display all three plots
#library(patchwork)
continental_plot high_seas_plot combined_plot# Protection fraction summary
# Read the data
prot_frac <- readRDS(here::here("Data/protect_fraction_summary_FUSE_03_continental.rds"))
sp <- fromJSON(here("Data", "shark_conservation_metrics_no_freshwater.json"))
sp_in_data <- read_csv(here("Data", "continental_puvsp_harmonised.csv"))
# First, get unique species mappings
species_mapping <- sp_in_data %>%
distinct(sp, species_name)
# Merge protection fractions with species names
prot_frac_with_names <- prot_frac %>%
rename(sp = Species_ID) %>% # rename to match sp_in_data column
left_join(species_mapping, by = "sp")
# Create species-FUSE mapping using the JSON data
species_FUSE_map <- data.frame(
Species = sp$FUSE$info$Species,
FUSE = as.numeric(sp$FUSE$info$FUSE)
)
# Add FUSE scores by species name
prot_frac_complete <- prot_frac_with_names %>%
left_join(species_FUSE_map, by = c("species_name" = "Species"))
# Create histogram for Mean_Protect_Fraction
hist_protect <- ggplot(prot_frac_complete, aes(x = Mean_Protect_Fraction)) +
geom_histogram(binwidth = 0.05, fill = "skyblue", color = "black") +
scale_x_continuous(limits=c(0,1)) +
theme_minimal() +
labs(title = "Histogram of Mean Protect Fraction\n(Continental)",
x = "Mean Protect Fraction",
y = "Count")
# Create histogram for FUSE
hist_fuse <- ggplot(prot_frac_complete, aes(x = FUSE)) +
geom_histogram(binwidth = 0.05, fill = "lightgreen", color = "black") +
theme_minimal() +
labs(title = "Histogram of FUSE Scores\n(Continental)",
x = "FUSE Score",
y = "Count")
# Create scatterplot
scatter_plot <- ggplot(prot_frac_complete, aes(x = FUSE, y = Mean_Protect_Fraction)) +
geom_point(alpha = 0.6, color = "darkblue") +
theme_minimal() +
scale_y_continuous(limits=c(0,1)) +
labs(title = "Scatterplot: FUSE vs Mean Protect Fraction (Continental)",
x = "FUSE Score",
y = "Mean Protect Fraction")
# Arrange plots in a grid
grid_plot <- grid.arrange(
hist_protect, hist_fuse, scatter_plot,
layout_matrix = rbind(c(1,2), c(3,3)),
widths = c(1, 1),
heights = c(1, 1)
)#High seas waters
# Protection fraction summary for high seas
# Read the data
prot_frac <- readRDS(here::here("Data/protect_fraction_summary_FUSE_03_highseas.rds"))
sp <- fromJSON(here("Data", "shark_conservation_metrics_no_freshwater.json"))
sp_in_data <- read_csv(here("Data", "highseas_puvsp_harmonised.csv"))
# First, get unique species mappings
species_mapping <- sp_in_data %>%
distinct(sp, species_name)
# Merge protection fractions with species names
prot_frac_with_names <- prot_frac %>%
rename(sp = Species_ID) %>% # rename to match sp_in_data column
left_join(species_mapping, by = "sp")
# Create species-FUSE mapping using the JSON data
species_FUSE_map <- data.frame(
Species = sp$FUSE$info$Species,
FUSE = as.numeric(sp$FUSE$info$FUSE)
)
# Add FUSE scores by species name
prot_frac_complete <- prot_frac_with_names %>%
left_join(species_FUSE_map, by = c("species_name" = "Species"))
# Create histogram for Mean_Protect_Fraction
hist_protect <- ggplot(prot_frac_complete, aes(x = Mean_Protect_Fraction)) +
geom_histogram(binwidth = 0.05, fill = "skyblue", color = "black") +
scale_x_continuous(limits=c(0,1)) +
theme_minimal() +
labs(title = "Histogram of Mean Protect Fraction\n(High Seas)",
x = "Mean Protect Fraction",
y = "Count")
# Create histogram for FUSE
hist_fuse <- ggplot(prot_frac_complete, aes(x = FUSE)) +
geom_histogram(binwidth = 0.05, fill = "lightgreen", color = "black") +
theme_minimal() +
labs(title = "Histogram of FUSE Scores\n(High Seas)",
x = "FUSE Score",
y = "Count")
# Create scatterplot
scatter_plot <- ggplot(prot_frac_complete, aes(x = FUSE, y = Mean_Protect_Fraction)) +
geom_point(alpha = 0.6, color = "darkblue") +
theme_minimal() +
scale_y_continuous(limits=c(0,1)) +
labs(title = "Scatterplot: FUSE vs Mean Protect Fraction (High Seas)",
x = "FUSE Score",
y = "Mean Protect Fraction")
# Arrange plots in a grid
grid_plot <- grid.arrange(
hist_protect, hist_fuse, scatter_plot,
layout_matrix = rbind(c(1,2), c(3,3)),
widths = c(1, 1),
heights = c(1, 1)
)#Now combine both and weigth by range size
library(tidyverse)
library(gridExtra)
library(jsonlite)
library(here)
# For the combined analysis part, modify similarly:
continental_prot_frac <- readRDS(here::here("Data/protect_fraction_summary_FUSE_03_continental.rds"))
highseas_prot_frac <- readRDS(here::here("Data/protect_fraction_summary_FUSE_03_highseas.rds"))
sp <- fromJSON(here("Data", "shark_conservation_metrics_no_freshwater.json"))
continental_sp_data <- read_csv(here("Data", "continental_puvsp_harmonised.csv"))
highseas_sp_data <- read_csv(here("Data", "highseas_puvsp_harmonised.csv"))
# Get species mappings for both datasets
continental_species_mapping <- continental_sp_data %>%
distinct(sp, species_name)
highseas_species_mapping <- highseas_sp_data %>%
distinct(sp, species_name)
# Add species names to both datasets
continental_prot_frac <- continental_prot_frac %>%
rename(sp = Species_ID) %>%
left_join(continental_species_mapping, by = "sp")
highseas_prot_frac <- highseas_prot_frac %>%
rename(sp = Species_ID) %>%
left_join(highseas_species_mapping, by = "sp")
# Calculate range sizes
continental_ranges <- continental_sp_data %>%
group_by(sp, species_name) %>%
summarise(continental_range = n(), .groups = "drop")
highseas_ranges <- highseas_sp_data %>%
group_by(sp, species_name) %>%
summarise(highseas_range = n(), .groups = "drop")
# Create species-FUSE mapping
species_FUSE_map <- data.frame(
Species = sp$FUSE$info$Species,
FUSE = as.numeric(sp$FUSE$info$FUSE)
)
# Combine the protection fractions with range sizes
combined_protection <- full_join(
continental_prot_frac %>%
select(sp, species_name, Mean_Protect_Fraction) %>%
rename(continental_protection = Mean_Protect_Fraction),
highseas_prot_frac %>%
select(sp, species_name, Mean_Protect_Fraction) %>%
rename(highseas_protection = Mean_Protect_Fraction),
by = c("sp", "species_name")
) %>%
# Join with the range sizes
left_join(continental_ranges, by = c("sp", "species_name")) %>%
left_join(highseas_ranges, by = c("sp", "species_name")) %>%
left_join(species_FUSE_map, by = c("species_name" = "Species"))
# Calculate weighted protection
combined_protection <- combined_protection %>%
mutate(
# Replace NA with 0 for protection values and ranges
continental_protection = replace_na(continental_protection, 0),
highseas_protection = replace_na(highseas_protection, 0),
continental_range = replace_na(continental_range, 0),
highseas_range = replace_na(highseas_range, 0),
# Calculate total range
total_range = continental_range + highseas_range,
# Calculate weighted protection
weighted_protection = (continental_protection * continental_range +
highseas_protection * highseas_range) /
total_range
)
# Add FUSE scores
combined_protection <- left_join(combined_protection, species_FUSE_map, by = c("species_name" = "Species"))
# Create summary statistics
summary_stats <- combined_protection %>%
select(-species_name) %>% # Remove the species name column as it's not numerical
summarise(across(everything(), list(
min = ~min(., na.rm = TRUE),
q1 = ~quantile(., 0.25, na.rm = TRUE),
median = ~median(., na.rm = TRUE),
mean = ~mean(., na.rm = TRUE),
q3 = ~quantile(., 0.75, na.rm = TRUE),
max = ~max(., na.rm = TRUE)
))) %>%
pivot_longer(everything(),
names_to = c("variable", "stat"),
names_pattern = "(.*)_(.*)") %>%
pivot_wider(names_from = stat, values_from = value)
# Create and format the flextable
library(flextable)
summary_table <- flextable(summary_stats) %>%
set_header_labels(
variable = "Variable",
min = "Minimum",
q1 = "1st Quartile",
median = "Median",
mean = "Mean",
q3 = "3rd Quartile",
max = "Maximum"
) %>%
colformat_double(digits = 3) %>% # Format numbers to 3 decimal places
theme_vanilla() %>%
autofit()
# Display the table
summary_tableVariable | Minimum | 1st Quartile | Median | Mean | 3rd Quartile | Maximum |
|---|---|---|---|---|---|---|
sp | 1.000 | 269.000 | 530.000 | 534.971 | 798.000 | 1,075.000 |
continental_protection | 0.000 | 0.302 | 0.308 | 0.352 | 0.336 | 1.000 |
highseas_protection | 0.000 | 0.000 | 0.000 | 0.101 | 0.000 | 1.000 |
continental_range | 0.000 | 56.000 | 193.000 | 1,248.928 | 650.000 | 40,875.000 |
highseas_range | 0.000 | 0.000 | 0.000 | 805.935 | 0.000 | 63,442.000 |
FUSE.x | 0.000 | 0.000 | 0.001 | 0.059 | 0.031 | 1.000 |
total_range | 1.000 | 56.000 | 200.000 | 2,054.864 | 655.000 | 104,317.000 |
weighted_protection | 0.300 | 0.303 | 0.309 | 0.356 | 0.341 | 1.000 |
FUSE.y | 0.000 | 0.000 | 0.001 | 0.059 | 0.031 | 1.000 |
# Create visualizations
hist_protect <- ggplot(combined_protection, aes(x = weighted_protection)) +
geom_histogram(binwidth = 0.05, fill = "skyblue", color = "black") +
scale_x_continuous(limits=c(0,1)) +
theme_minimal() +
labs(title = "Histogram of Range-Weighted Protection \nFraction (Combined)",
x = "Weighted Protection Fraction",
y = "Count")
hist_fuse <- ggplot(combined_protection, aes(x = FUSE.x)) +
geom_histogram(binwidth = 0.05, fill = "lightgreen", color = "black") +
theme_minimal() +
labs(title = "Histogram of FUSE Scores",
x = "FUSE Score",
y = "Count")
scatter_plot <- ggplot(combined_protection, aes(x = FUSE.x, y = weighted_protection)) +
geom_point(alpha = 0.6, color = "darkblue") +
theme_minimal() +
scale_y_continuous(limits=c(0,1)) +
labs(title = "Scatterplot: FUSE vs Weighted Protection Fraction (Combined)",
x = "FUSE Score",
y = "Weighted Protection Fraction")
# Create species range type summary
range_type_summary <- combined_protection %>%
summarise(
total_species = n(),
continental_only = sum(highseas_range == 0 & continental_range > 0),
highseas_only = sum(continental_range == 0 & highseas_range > 0),
both_ranges = sum(continental_range > 0 & highseas_range > 0)
) %>%
pivot_longer(everything(),
names_to = "Distribution Type",
values_to = "Number of Species")
# Create and format the flextable
range_type_table <- flextable(range_type_summary) %>%
set_header_labels(
`Distribution Type` = "Distribution Type",
`Number of Species` = "Number of Species"
) %>%
theme_vanilla() %>%
autofit()
# Display the table
range_type_tableDistribution Type | Number of Species |
|---|---|
total_species | 1,005 |
continental_only | 802 |
highseas_only | 5 |
both_ranges | 198 |
# Arrange plots in a grid
grid_plot <- grid.arrange(
hist_protect, hist_fuse, scatter_plot,
layout_matrix = rbind(c(1,2), c(3,3)),
widths = c(1, 1),
heights = c(1, 1)
)# Save the combined protection data
saveRDS(combined_protection, file = here::here("Data", "combined_protection_analysis.rds"))